Performance analysis of massively parallel embedded hardware architectures for retinal image processing

This paper examines the implementation of a retinal vessel tree extraction technique on different hardware platforms and architectures. Retinal vessel tree extraction is a representative application of those found in the domain of medical image processing. The low signal-to-noise ratio of the images leads to a large amount of low-level tasks in order to meet the accuracy requirements. In some applications, this might compromise computing speed. This paper is focused on the assessment of the performance of a retinal vessel tree extraction method on different hardware platforms. In particular, the retinal vessel tree extraction method is mapped onto a massively parallel SIMD (MP-SIMD) chip, a massively parallel processor array (MPPA) and onto an field-programmable gate arrays (FPGA)This work is funded by Xunta de Galicia under the projects 10PXIB206168PR and 10PXIB206037PR and the program Maria BarbeitoS

Assessment of human enteric viruses in cultured and wild bivalve molluscs

Standard and real-time reverse transcription-PCR (rRT-PCR) procedures were used to monitor cultured and wild bivalve molluscs from the Ría de Vigo (NW Spain) for the main human enteric RNA viruses, specifically, norovirus (NoV), hepatitis Avirus (HAV), astrovirus (AsV), rotavirus (RT), enterovirus (EV), and Aichi virus (AiV). The results showed the presence of at least one enteric virus in 63.4% of the 41 samples analyzed. NoV GII was the most prevalent virus, detected in 53.7% of the samples, while NoV GI, AsV, EV, and RV were found at lower percentages (7.3, 12.2, 12.2, and 4.9%, respectively). In general, samples obtained in the wild were more frequently contaminated than those from cultured (70.6 vs. 58.3%) molluscs and were more readily contaminated with more than one virus. However, NoV GI was detected in similar amounts in cultured and wild samples (6.4 × 102 to 3.3 × 103 RNA copies per gram of digestive tissue) while the concentrations of NoV GII were higher in cultured (from 5.6 × 101 to 1.5 × 104 RNA copies per gram of digestive tissue) than in wild (from 1.3 × 102 to 3.4 × 104 RNA copies per gram of digestive tissue) samples. [Int Microbiol 2009; 12(3):145-151

Implementation of a motion estimation algorithm for Intel FPGAs using OpenCL

Producción CientíficaMotion Estimation is one of the main tasks behind any video encoder. It is a compu- tationally costly task; therefore, it is usually delegated to specific or reconfigurable hardware, such as FPGAs. Over the years, multiple FPGA implementations have been developed, mainly using hardware description languages such as Verilog or VHDL. Since programming using hardware description languages is a complex task, it is desirable to use higher-level languages to develop FPGA applications.The aim of this work is to evaluate OpenCL, in terms of expressiveness, as a tool for devel- oping this kind of FPGA applications. To do so, we present and evaluate a parallel implementation of the Block Matching Motion Estimation process using OpenCL for Intel FPGAs, usable and tested on an Intel Stratix 10 FPGA. The implementa- tion efficiently processes Full HD frames completely inside the FPGA. In this work, we show the resource utilization when synthesizing the code on an Intel Stratix 10 FPGA, as well as a performance comparison with multiple CPU implementations with varying levels of optimization and vectorization capabilities. We also compare the proposed OpenCL implementation, in terms of resource utilization and perfor- mance, with estimations obtained from an equivalent VHDL implementation.Junta de Castilla y León - Consejería de Educación de la Proyecto PROPHET-2 (VA226P20)Ministerio de Economía, Industria y Competitividad: (PID2019- 104834 GB-I00) and European Regional Development Fund (ERDF) program: Project PCAS (TIN2017-88614-R)Ministerio de Ciencia e Innovación (PID2019-104184RB-I00 / AEI / 10.13039/501100011033)Xunta de Galicia y fondos FEDER de la UE (Centro de Investigación de Galicia acreditación 2019-2022, ref. ED431G 2019/01; Consolidation Program of Competitive Reference Groups, ref. ED431C 2021/30Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación y “European Union NextGenerationEU/PRTR” : (MCIN/ AEI/10.13039/501100011033) - grant TED2021-130367B-I00Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL